Perspectives on nutrient management in arable farming systems
Bibliographic record
Abstract
Abstract. Sustainable nutrient management includes economizing on finite natural resources such as fossil energy and limited phosphorus (P) resources. Arable farms with exclusively crop production are characterized by large nutrient export in farm products. In the long term, nutrient export from soils and losses must be balanced by adequate inputs of fertilizers, biological N 2 fixation or recycled products from the human food system. Critical issues associated in particular with arable systems discussed in this paper include organic matter depletion and lack of synchronization between nitrogen (N) release from organic N pools (such as plant residues and green manures) and crop demand, leading to N losses and/or N deficiency. Further critical issues identified include efficient use of indigenous soil P and applied P fertilizers and, especially in organic systems, risk of depletion of P and K, which if realized reduce soil fertility and limit production. The risk of enrichment of trace elements to levels toxic to soil microbes, plants, animals or man is also discussed. Suggested measures for managing these critical issues include choice of crop rotation, residue and green manure management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".